#   scipy-yli: Helpful SciPy utilities and recipes
#   Copyright © 2022–2023  Lee Yingtong Li (RunasSudo)
#
#   This program is free software: you can redistribute it and/or modify
#   it under the terms of the GNU Affero General Public License as published by
#   the Free Software Foundation, either version 3 of the License, or
#   (at your option) any later version.
#
#   This program is distributed in the hope that it will be useful,
#   but WITHOUT ANY WARRANTY; without even the implied warranty of
#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#   GNU Affero General Public License for more details.
#
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#   along with this program.  If not, see <https://www.gnu.org/licenses/>.

from pytest import approx

import numpy as np
import pandas as pd

import yli

def test_chi2_ol10_15():
	"""Compare yli.chi2 for Ott & Longnecker (2016) example 10.15"""
	
	data = [
		(1, 'Moderate', 15),
		(2, 'Moderate', 32),
		(3, 'Moderate', 18),
		(4, 'Moderate', 5),
		(1, 'Mildly Severe', 8),
		(2, 'Mildly Severe', 29),
		(3, 'Mildly Severe', 23),
		(4, 'Mildly Severe', 18),
		(1, 'Severe', 1),
		(2, 'Severe', 20),
		(3, 'Severe', 25),
		(4, 'Severe', 22)
	]
	
	df = pd.DataFrame({
		'AgeCategory': np.repeat([d[0] for d in data], [d[2] for d in data]),
		'Severity': np.repeat([d[1] for d in data], [d[2] for d in data])
	})
	
	result = yli.chi2(df, 'Severity', 'AgeCategory')
	assert result.statistic == approx(27.13, abs=0.01)
	assert result.pvalue == approx(0.00014, abs=0.00001)

def test_chi2_ol10_18():
	"""Compare yli.chi2 for Ott & Longnecker (2016) example 10.18"""
	
	data = [
		(False, False, 250),
		(True, False, 750),
		(False, True, 400),
		(True, True, 1600)
	]
	
	df = pd.DataFrame({
		'Response': np.repeat([d[0] for d in data], [d[2] for d in data]),
		'Stress': np.repeat([d[1] for d in data], [d[2] for d in data])
	})
	
	result = yli.chi2(df, 'Stress', 'Response')
	assert result.oddsratio.point == approx(1.333, abs=0.001)
	assert result.oddsratio.ci_lower == approx(1.113, abs=0.001)
	assert result.oddsratio.ci_upper == approx(1.596, abs=0.001)
	
	expected_summary = '''Stress    False  True 
Response              
False       250    400
True        750   1600

χ²(1) = 9.82; p = 0.002*
OR (95% CI) = 1.33 (1.11–1.60)
RR (95% CI) = 1.11 (1.03–1.18)'''
	
	assert result.summary() == expected_summary
	assert result._repr_html_() == '<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border="1" class="dataframe">\n  <thead>\n    <tr style="text-align: right;">\n      <th>Stress</th>\n      <th>False</th>\n      <th>True</th>\n    </tr>\n    <tr>\n      <th>Response</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>False</th>\n      <td>250</td>\n      <td>400</td>\n    </tr>\n    <tr>\n      <th>True</th>\n      <td>750</td>\n      <td>1600</td>\n    </tr>\n  </tbody>\n</table>\n</div><br><i>χ</i><sup>2</sup>(1) = 9.82; <i>p</i> = 0.002*<br>OR (95% CI) = 1.33 (1.11–1.60)<br>RR (95% CI) = 1.11 (1.03–1.18)'